---inference_mode:是否在推理模式下使用Peft模型。 ---prefix_projection:是否投影前缀嵌入(token),---默认值为false,表示使用P-Tuning v2, ---如果为true,则表示使用 Prefix Tuning。 第三步,通过调用get_peft_model方法包装基础的Transformer模型。 model = AutoModelForCausalLM.from_pretrained(model_name_or...
from peft import AutoPeftModelForCausalLMmodel = AutoPeftModelForCausalLM.from_pretrained( args.output_dir, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()# S...
from_pretrained(model_name_or_path, low_cpu_mem_usage=True, torch_dtype=torch.float32, device_map="cuda:1", trust_remote_code=True).eval() tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side='left') tokenizer.pad_token_id = 0 tokenizer.bos_token_id = 1 peft_...
model = get_peft_model(model, peft_config) Training Model loading from pre-trained - OOM: pretrained = 'saved_dir' config = PeftConfig.from_pretrained(pretrained) model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-xl',low_cpu_mem_usage=True) model = PeftModel.from_pretrained(m...
下面是保存模型的正确方法。它也会保存标记器配置。
loaded before calling `from_pretrained`. kwargs: (`optional`): Additional keyword arguments passed along to the specific Lora configuration class. Additional keyword arguments passed along to the specific PEFT configuration class. """ from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE...
peft_model = PeftModel.from_pretrained(base_model,local_path_to_model_adapter) It would be helpful to describe both within peft documentation. More specifically: Highlight thatmerge_and_unloaddoes not work with AutoModelforCausalLM. Clarify how AutoModelforCausalLM actually loads the adapter (I ...